Mastering Agentic AI: From Prompt to Protocols to Production
Architect autonomous AI systems: ReAct loops, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, cognitive memory, LangGraph, CrewAI, and production observability.
Two Flexible Ways to Master Agentic AI
Choose between self-paced on-demand videos on Udemy or live weekly cohort mentorship with real-time swarm labs.
Buy Directly on Udemy
Learn at your own pace with lifetime access to 38+ hours, 339 lectures, 59 downloadable MCP templates, and Q&A support.
- Raw Python ReAct loops, planning & structured JSON outputs
- Model Context Protocol (MCP) server & client development
- Multi-agent swarm orchestration with LangGraph & CrewAI
- 59 downloadable resources + Udemy Certificate
Join the Live Cohort
Weekly live Zoom architecture workshops with Vinit Singh, 1-on-1 office hours, private Discord agent lab, and custom swarm reviews.
- Includes everything in the On-Demand course +
- 6 weeks of live interactive Zoom coding sessions & Q&A
- 1-on-1 weekly instructor office hours & live MCP server debugging
- Private Discord peer cohort & production swarm capstone reviews
- Official gadgap AI Certified Agentic AI Architect Credential
Course Description & Overview
Chatbots answer questions; Autonomous Agents get real work done. This comprehensive 38-hour masterclass is designed for AI engineers, software developers, and data scientists looking to transition from basic single-turn prompts to architecting production-grade autonomous agent systems.
You will master perception, reasoning, planning, and action loops (ReAct, Plan-and-Solve, Tree of Thoughts), build custom Model Context Protocol (MCP) servers and clients, design hierarchical multi-agent teams with LangGraph and CrewAI, implement episodic & semantic memory architectures, and deploy resilient swarms with enterprise observability (LangSmith, OpenTelemetry, Prometheus, Jaeger).
Course Requirements & Prerequisites
- Python & APIs: Familiarity with Python and basic API / HTTP concepts.
- LLM Basics: Basic understanding of LLM prompting and API keys.
- Goal: Eagerness to architect multi-step autonomous workflows and tools.
Who This Course Is For (Intended Learners)
Built for engineers and builders ready to transition into autonomous multi-agent engineering.
AI Engineers & Software Developers
Developers wanting to build robust, goal-directed AI systems that plan, call tools via MCP, and handle complex non-deterministic failures safely.
Enterprise Architects & Technical Leads
Leaders designing scalable multi-agent systems using Model Context Protocol (MCP), LangGraph state machines, and distributed queue workers.
Automation Specialists & RPA Engineers
Engineers looking to replace fragile legacy RPA scripts with intelligent, self-correcting agents capable of code execution and web browsing.
Founders & Agentic Product Builders
Creators building autonomous coding assistants, deep-research bots, automated SDR agents, and collaborative multi-agent SaaS platforms.
Comprehensive 6-Module Curriculum (339 Lectures)
- From static prompts to dynamic agent loops: Perception, Reasoning, Planning, and Action
- ReAct (Reason + Act) paradigm: Parsing thoughts, actions, and observations
- Structured outputs: Pydantic schemas, JSON-mode, and instructor libraries
- Handling error loops, rate limits, and non-deterministic agent failures
- Native LLM function calling (OpenAI, Anthropic Claude, Gemini function declarations)
- Model Context Protocol (MCP): Open standard for agent-to-tool and agent-to-data communication
- Building custom MCP servers for SQL databases, GitHub, web scrapers, and internal APIs
- Security sandboxing: Preventing prompt injection, unauthorized tool execution, and data leaks
- Short-term working memory: Context window compression and message summarization
- Episodic memory: Storing past task trajectories and outcomes in vector embeddings
- Semantic & procedural memory: Maintaining knowledge graphs and operating procedures
- Hierarchical RAG (Retrieval Augmented Generation) for real-time factual grounding
- Multi-agent patterns: Hierarchical supervisor, peer-to-peer delegation, and competitive debate
- LangGraph: State graphs, cyclic workflows, checkpoints, and time-travel debugging
- CrewAI & AutoGen: Defining distinct agent personas, goals, backstories, and task dependencies
- Consensus mechanisms, task handoffs, and resolving agent deadlocks
- Safe code execution sandboxes (E2B, Docker, WebAssembly)
- Self-correction loops: Running unit tests, capturing tracebacks, and autonomous code fixing
- Multi-modal agents: Browsing web pages, analyzing screenshots, and interacting with UI elements
- Human-in-the-loop (HITL) authorization gates for critical actions
- Agent observability and tracing with LangSmith, Phoenix Arize, and OpenTelemetry
- Benchmarking agent performance: Task completion rate, token cost efficiency, and latency
- Optimizing token overhead and routing queries between fast and deep reasoning models
- Capstone: Deploy an Enterprise Autonomous Market Research & Lead Generation Swarm
Vinit Singh
AI Consultant & Principal AI Architect, gadgap AI
Vinit is an AI Consultant and Educator specializing in LLM Fine-Tuning, Voice AI, Speech Language Models, Agentic AI, and Computer Vision — with over 18 years of experience in Data Science and Artificial Intelligence. A graduate of IIT Bombay with Stanford Machine Learning and Deep Learning certifications, he works at the intersection of AI research and real-world deployment.
Currently a Consultant in the Speech & Language team at Sony India Software Centre, his prior work spans Computer Vision on Nvidia edge hardware at Assert AI, and end-to-end AI consulting at tvam Technologies — where he built agentic FinTech workflows, a robo-advisor via LoRA fine-tuning on DeepSeek-R1, and a Voice AI telecaller pipeline.
A top 3% Udemy creator globally, trusted by learners across 150+ countries and enterprises including Adidas, Barclays, and Volkswagen — covering Voice AI, Agentic AI, Computer Vision, and NLP & LLMs.
Enroll in Agentic AI
Buy on Udemy for on-demand self-paced access, or join our live interactive cohort for hands-on mentorship.